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ginkida

portainer-mcp

by ginkida

portainer_stack_status

Check the health of a Docker stack by summarizing service replicas, update state, and recent task failures. Works on Swarm stacks and falls back to Compose containers, including cron-driven services.

Instructions

Health summary of a stack: every service with running/desired replicas, update state and the most recent task failures.

Start here when asked "is stack X ok / why is X down". Works on Swarm (services + tasks) and falls back to container states on a standalone Compose endpoint. Cron-driven services are judged on their last run, and only failures newer than the last good task are reported.

Args: stack_name: Stack name (Swarm namespace / Compose project) endpoint_id: Target endpoint ID (uses default if omitted)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
stack_nameYes
endpoint_idNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Addedv0.8.0

TDQS

A5/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden and does so excellently. It discloses the exact output contents, the fallback to container states on Compose, cron-driven behavior, and the failure-reporting logic (only failures newer than the last good task). This is thorough behavioral transparency beyond basic read-only hints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: a crisp opening summary, followed by usage guidance, behavioral details, and a clean Args block. Every sentence earns its place, and the key purpose is front-loaded. It is concise yet information-dense without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers purpose, usage context, environment-specific behavior, and parameter semantics. Given an output schema exists (as indicated), the description does not need to explain return values. It is complete for an agent to correctly select and invoke this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description's Args section fully explains both parameters: stack_name as 'Stack name (Swarm namespace / Compose project)' and endpoint_id as 'Target endpoint ID (uses default if omitted)'. This adds significant meaning beyond the raw schema, making the parameters self-explanatory.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Health summary of a stack' with specific details about replicas, update state, and task failures. It also differentiates itself from siblings by saying 'Start here when asked is stack X ok / why is X down', distinguishing it from inspect, logs, and other stack tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use guidance: 'Start here when asked is stack X ok / why is X down'. It also explains behavior across Swarm and Compose environments, giving the agent clear context for invocation. While it doesn't name alternative tools, the directive is strong and unambiguous.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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